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Bosque (Valdivia)

versión On-line ISSN 0717-9200

Resumen

SALAS, Christian; ENE, Liviu; OJEDA, Nelson  y  SOTO, Héctor. Parametric and non-parametric statistical methods for predicting plotwise variables based on Landsat ETM+: a comparison in an Araucaria araucana forest in Chile. Bosque (Valdivia) [online]. 2010, vol.31, n.3, pp.179-194. ISSN 0717-9200.  http://dx.doi.org/10.4067/S0717-92002010000300002.

The Araucaria araucana forests have a high level of both ecological and scientific importance, because they are long-lived and endemic. Although there have been several ecological studies conducted concerning A. araucana forests, none has produced quantitative models. We compared parametric and non-parametric statistical methods for predicting stand variables from Landsat ETM+ derived variables from two A. araucana stands in south-central Chile. The assessed parametric methods were multiple linear regressions (MLR), generalized least squares with a non-null correlation structure (GLS), linear mixed-effects models (LME), and partial least squares (PLS); while the non-parametric methods were: k-nearest neighbor (k-NN) and most similar neighbor (MSN). In descending order, number of trees per ha (N), stand gross volume (V), stand basal area (G), and dominant height (Hdom) were the most difficult variables to be modeled by all the methods. LME with known random effects (i.e., LME1) performed best, achieving a root mean square showing differences (RMSD) for N and V of 18.31 and 4.08 % versus 33.06 and 33.05 % for the second-best method, respectively. However, within the parametric methods, LME1 cannot be used for predicting new observations with no data. After LME1, GLS performed the best; also accounting for the spatial correlation of the data. Parametric methods achieved lower errors. Furthermore, differences were greater among non-parametric than those among parametric methods, with a difference of 10-15 % between k-NN and MSN. Although, given our results, we favor parametric methods; we point out that non-parametric methods are also useful, and the choice between parametric and non-parametric methods depends on the ultimate objective of the study.

Palabras clave : k-nearest neighbor estimation; most similar neighbor; partial least squares regression; remote sensing.

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